Many relevant works focus on unsupervised domain adaptation for general CV tasks. Co-training [1]} uses two classifiers and two data views to generate labels for unlabeled samples. Both tri-training [2]} and asymmetric tri-training [3]} extend co-training by using three classifiers to generate labels, i.e., labels will be added if two classifiers make an agreement. Some approaches try to learn domain-invariant representations by selecting similar instances between domains or adding a classifier to distinguish domains [4]}, [5]}. ADDA [6]} leverages the Generative Adversarial Networks (GANs) loss on domain label to train a new network. CDAN [7]} applies conditional adversarial learning which combines features and labels using a multilinear mapping.
